Carsten Q. Schneider

Standards of Good Practice in Qualitative Comparative Analysis (QCA) and Fuzzy-Sets

As a relatively new methodological tool, QCA is still a work in progress. Standards of good practice are needed in order to enhance the quality of its applications. We present a list from A to Z of twenty-six proposals regarding what a “good” QCAbased research entails, both with regard to QCA as a research approach and as an analytical technique. Our suggestions are subdivided into three categories: criteria referring to the research stages before, during, and after the analytical moment of data analysis. Th is listing can be read as a guideline for authors, reviewers, and readers of QCA.

Combining QCA and Process Tracing in Set-Theoretic Multi-Method Research

Set-theoretic methods and Qualitative Comparative Analysis (QCA) in particular are case-based methods. There are, however, only few guidelines on how to combine them with qualitative case studies. Contributing to the literature on multi-method research (MMR), we offer the first comprehensive elaboration of principles for the integration of QCA and case studies with a special focus on case selection. We show that QCAs reliance on set-relational causation in terms of necessity and sufficiency has important consequences for the choice of cases. Using real world data for both crisp-set and fuzzy-set QCA, we show what typical and deviant cases are in QCA-based MMR. In addition, we demonstrate how to select cases for comparative case studies aiming to discern causal mechanisms and address the puzzles behind deviant cases. Finally, we detail the implications of modifying the set-theoretic cross-case model in the light of case-study evidence. Following the principles developed in this article should increase the inferential leverage of set-theoretic MMR.

Qualitative Comparative Analysis (QCA) and Fuzzy-Sets: Agenda for a Research Approach and a Data Analysis Technique

“Qualitative Comparative Analysis” (QCA) is an increasingly applied methodological tool in comparative social sciences. It is well suited for the analysis of causally complex claims framed in terms of necessity and sufficiency. This article presents the epistemology of QCA and discusses its applicability to social science research questions. It also illustrates some of the features that have recently been added to this set of methodological tools. This article is best read in close conjunction with Schneider and Wagemann’s “Standards of Good QCA Practice,” the next paper in this journal issue.

An Introduction to Crisp Set QCA, with a Comparison to Binary Logistic Regression

The authors focus on the dichotomous crisp set form of qualitative comparative analysis (QCA). The authors review basic set theoretic QCA methodology, including truth tables, solution formulas, and coverage and consistency measures and discuss how QCA (a) displays relations between variables, (b) highlights descriptive or complex causal accounts for specific (groups of) cases, and (c) expresses the degree of fit. To help readers determine when QCA’s configurational approach might be appropriate, the authors compare and contrast QCA to mainstream statistical methodologies such as binary logistic regressions done on the same data set.

Liberalization, transition and consolidation: measuring the components of democratization

This article measures the process of democratization by subdividing it into three components: the liberalization of autocracy, the mode of transition and the consolidation of democracy. The 30 or so countries included in the study are situated in different world regions, mainly southern and eastern Europe, south and central America and the former Soviet Union – all of which have experienced regime transitions since 1974. The study also includes a sample of countries from the Middle East and northern Africa that are, at best, only in an embryonic stage of liberalization. Measured by scalograms, the data provide comparative indicators of the progress each country has achieved over the period 1974–2000. The study tests this time series for ‘patterns’, guided by the hypothesis that the multiple dimensions of liberalization, transition and consolidation are consistently related to each other, both temporally and spatially. The findings indicate a single underlying dimensional structure to the data. This allows separate scales for liberalization and consolidation to be created and combined into a general indicator of democratization. Contrary to expectations in the literature, most central and eastern European countries perform comparatively better than the southern European and Latin American cases. Not only do they reach the same high levels of liberalization and consolidation, but they also do so in a much shorter time span. Furthermore, there is compelling evidence in the Middle Eastern and North African data that the liberalization of autocratic regimes does not always play a democratization triggering role. Department of Political Science, Central European University, Naderutca 9, H-1051 Budapest, Hungary, E-mail: philippe.schmitter@iue.it

Case Studies Nested in Fuzzy-set QCA on Sufficiency Formalizing Case Selection and Causal Inference

Qualitative Comparative Analysis (QCA) is a method for cross-case analyses that works best when complemented with follow-up case studies focusing on the causal quality of the solution and its constitutive terms, the underlying causal mechanisms, and potentially omitted conditions. The anchorage of QCA in set theory demands criteria for follow-up case studies that are different from those known from regression-based multimethod research (MMR). Based on the evolving research on set-theoretic MMR, we introduce principles for formalized case selection and causal inference after a fuzzy-set QCA on sufficiency. Using an empirical example for illustration, we elaborate on the principles of counterfactuals for intelligible causal inference in the analysis of three different types of cases. Furthermore, we explain how case-based counterfactual inferences on the basis of QCA solutions are related to counterfactuals in the course of processing a truth table in order to produce a solution. We then flesh out two important functions that ideal types play for QCA-based case studies: First, they inform the development of formulas for the choice of the best available cases for with-case analysis and, second, establish the boundaries of generalization of the causal inferences.

Standards guter Praxis in Qualitative Comparative Analysis (QCA) und Fuzzy-Sets

Seit einigen Jahren ist auch im deutschsprachigen Raum ein vermehrtes Interesse an einer Methodenfamilie zu beobachten, die generell unter dem Akronym ‚QCA’ bekannt ist. ‚QCA’ steht fur ‚Qualitative Comparative Analysis’ und wurde von dem amerikanischen Sozialwissenschaftler Charles Ragin in den 1980er Jahren erstmals einer breiteren Offentlichkeit zuganglich gemacht (Ragin 1987). Seitdem hat Ragin die Technik mehrmals modifiziert, erweitert und verfeinert (Ragin 2000; 2008b), so dass diese Familie der Datenanaly-severfahren mittlerweile einen hohen Bekanntheitsgrad erhalten hat. In diesem Beitrag stellen wir zuerst den ‚State of the Art’ hinsichtlich QCA vor und gehen dabei sowohl auf seine Grundprinzipien als auch auf die verschiedenen Varianten ‚konfigurativer vergleichender Methoden’ (Rihoux/Ragin 2008) ein. Danach unterbreiten wir eine Liste mit Kriterien fur eine ‚gute’ QCA-Analyse. Dieser Beitrag soll somit AnwenderInnen von QCA als Richtlinie gelten, was sie bei der Durchfuhrung einer QCA-Analyse beachten sollten, damit diese als qualitativ gut und technisch korrekt gelten kann. Daruber hinaus soll der von uns zur Diskussion gestellte Standard guter QCA-Praxis ein hilfreiches Instrument fur LeserInnen und KommentatorInnen bei der Beurteilung von Arbeiten sein, die auf QCA beruhen.

Real Differences and Overlooked Similarities Set-Methods in Comparative Perspective

Jack Paine (JP) and Alrik Thiem, Michael Baumgartner, and Damien Bol (TBB) provide diametrically opposed answers to the hotly debated question as to whether set-theory-based methods constitute a family of methods sui generis or whether not only set methods can be subsumed under the existing statistical framework, but also, if so, should be abandoned. I find TBB’s argument convincing that due to their different mathematical foundations, these two families of methods cannot be directly translated, let alone unified into one. Notwithstanding this, it seems clear to me that work must continue on identifying conceptual similarities and differences, and to elaborate on each method’s respective strengths and weaknesses. Because I mostly agree with TBB, I only briefly comment on some of their claims and then dedicate the rest of the text to JP.

Fuzzy-Set Qualitative Comparative Analysis (fs/QCA): Ein Zwei-Stufen-Modul

Die heutigen Sozialwissenschaften sind von einer eigentumlichen und scheinbar unuberwindbaren Zweiteilung gepragt: Die Unterscheidung in ,quantitative‘ und ,qualitative‘ Forschung schlagt sich in Berufungsverfahren, Projektantragen und Lehrbuchern nieder, und pragt nicht zuletzt auch Forscherbiografien. Dabei greifen quantitativ arbeitende (oder ,variablen-orientierte‘) Sozialwissenschaftler im Regelfall auf die bekannten statistischen Verfahren zuruck, die es ihnen erlauben, grose Anzahlen von Fallen zu untersuchen1. Hierbei handelt es sich meistens um eine Stichprobe aus einer vorher festgelegten Population (Grundgesamtheit). Die Abgrenzung qualitativer (oder ,einzelfall-orientierter‘) Methoden ist im Gegensatz dazu ungleich schwieriger. Qualitative Methoden umfassen so unterschiedliche Varianten wie Langzeitbeobachtungen, diskursanalytische Verfahren, Experteninterviews und eher hermeneutische Zugangsweisen. Gemeinsam ist ihnen eine in der Regel (sehr) kleine Fallanzahl, die es erlaubt, eine detaillierte Erfassung des Einzelfalls zu bewaltigen.

A Unifying Framework for Causal Analysis in Set-Theoretic Multimethod Research

The combination of Qualitative Comparative Analysis (QCA) with process tracing, which we call set-theoretic multimethod research (MMR), is steadily becoming more popular in empirical research. Despite the fact that both methods have an elected affinity based on set theory, it is not obvious how a within-case method operating in a single case and a cross-case method operating on a population of cases are compatible and can be combined in empirical research. There is a need to reflect on whether and how set-theoretic MMR is internally coherent and how QCA and process tracing can be integrated in causal analysis. We develop a unifying foundation for causal analysis in set-theoretic MMR that highlights the roles and interplay of QCA and process tracing. We argue that causal inference via counterfactuals on the level of single cases integrates QCA and process tracing and assigns proper and equally valuable roles to both methods.